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整合多组学和机器学习方法揭示了结直肠癌中氨基酸及其衍生物相关特征的代谢情况。

Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer.

作者信息

Yue Jian, Fang Huiying, Yang Qian, Feng Rui, Ren Guosheng

机构信息

Department of Breast and Thyroid Surgery, Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Department of Breast Surgery, Gaozhou People's Hospital, Gaozhou, Guangdong, China.

出版信息

Front Oncol. 2025 Mar 26;15:1565090. doi: 10.3389/fonc.2025.1565090. eCollection 2025.

DOI:10.3389/fonc.2025.1565090
PMID:40206583
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11978647/
Abstract

OBJECTIVE

The metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC.

METHODS

This study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation.

RESULTS

We successfully constructed prognostic models and Nomograms based on key MAAD-related molecules. There was a notable survival benefit observed for low-risk patients when contrasted with their high-risk counterparts. In addition, the high-risk group had a poorer response to immunotherapy and stronger tumor heterogeneity compared with the low-risk group. Further research found that by knocking down the MAAD-related gene. LSM8, the malignant characteristics of colorectal cancer cell lines were significantly alleviated, suggesting that LSM8 may become a potential therapeutic target.

CONCLUSION

The MAAD-related gene LSM8 is likely involved in the progression of CRC and could be a hopeful target for therapeutic intervention.

摘要

目的

氨基酸及其衍生物代谢(MAAD)与结直肠癌(CRC)的发生发展密切相关,但其具体调控机制尚不清楚。本研究旨在探讨MAAD在结直肠癌进展中的作用,并最终确定可能成为CRC潜在治疗靶点的关键分子。

方法

本研究整合批量转录组和单细胞转录组,从多个层面分析和鉴定与MAAD相关的关键基因。随后,采用多种机器学习方法构建与MAAD相关的预后模型,并分析不同模式下免疫细胞浸润、肿瘤异质性、肿瘤突变负担及潜在通路变化。最后,鉴定关键分子进行实验验证。

结果

我们成功构建了基于关键MAAD相关分子的预后模型和列线图。与高风险患者相比,低风险患者有显著的生存获益。此外,与低风险组相比,高风险组对免疫治疗的反应较差,肿瘤异质性更强。进一步研究发现,敲低与MAAD相关的基因LSM8后,结直肠癌细胞系的恶性特征明显减轻,提示LSM8可能成为潜在的治疗靶点。

结论

与MAAD相关的基因LSM8可能参与CRC的进展,有望成为治疗干预的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/a7bf2bb67af2/fonc-15-1565090-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/0a095db74f8a/fonc-15-1565090-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/b805980ae47e/fonc-15-1565090-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/b170e275bd41/fonc-15-1565090-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/4b70b6ecc478/fonc-15-1565090-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/9e50dd6d898a/fonc-15-1565090-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/02bf4214e451/fonc-15-1565090-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/1f073ea175ec/fonc-15-1565090-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/52e55331f061/fonc-15-1565090-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/a7bf2bb67af2/fonc-15-1565090-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/0a095db74f8a/fonc-15-1565090-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/b805980ae47e/fonc-15-1565090-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/b170e275bd41/fonc-15-1565090-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/4b70b6ecc478/fonc-15-1565090-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/9e50dd6d898a/fonc-15-1565090-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/02bf4214e451/fonc-15-1565090-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/1f073ea175ec/fonc-15-1565090-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/52e55331f061/fonc-15-1565090-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f384/11978647/a7bf2bb67af2/fonc-15-1565090-g009.jpg

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2
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Front Immunol. 2024 Dec 5;15:1516362. doi: 10.3389/fimmu.2024.1516362. eCollection 2024.
3
Knockdown of integrin β1 inhibits proliferation and promotes apoptosis in bladder cancer cells.
整合素β1的敲低抑制膀胱癌细胞的增殖并促进其凋亡。
Biofactors. 2025 Jan-Feb;51(1):e2150. doi: 10.1002/biof.2150. Epub 2024 Dec 7.
4
Biomarkers in Colorectal Cancer: Actual and Future Perspectives.结直肠癌的生物标志物:现状与未来展望。
Int J Mol Sci. 2024 Oct 27;25(21):11535. doi: 10.3390/ijms252111535.
5
Eliminating a barrier: Aiming at VISTA, reversing MDSC-mediated T cell suppression in the tumor microenvironment.消除一个障碍:以VISTA为靶点,逆转肿瘤微环境中髓源性抑制细胞介导的T细胞抑制作用。
Heliyon. 2024 Aug 30;10(17):e37060. doi: 10.1016/j.heliyon.2024.e37060. eCollection 2024 Sep 15.
6
A systematic review of the gut microbiome, metabolites, and multi-omics biomarkers across the colorectal cancer care continuum.系统综述了结直肠癌诊治全流程中的肠道微生物组、代谢物和多组学生物标志物。
Benef Microbes. 2024 Aug 14;15(6):539-563. doi: 10.1163/18762891-bja00026.
7
Unlocking the potential: Targeting metabolic pathways in the tumor microenvironment for Cancer therapy.释放潜能:靶向肿瘤微环境中的代谢途径用于癌症治疗。
Biochim Biophys Acta Rev Cancer. 2024 Sep;1879(5):189166. doi: 10.1016/j.bbcan.2024.189166. Epub 2024 Aug 5.
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Metabolic heterogeneity in tumor microenvironment - A novel landmark for immunotherapy.肿瘤微环境中的代谢异质性——免疫治疗的新标志物。
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9
A developmental constraint model of cancer cell states and tumor heterogeneity.癌症细胞状态和肿瘤异质性的发育约束模型。
Cell. 2024 Jun 6;187(12):2907-2918. doi: 10.1016/j.cell.2024.04.032.
10
Mechanisms of metastatic colorectal cancer.转移性结直肠癌的发病机制。
Nat Rev Gastroenterol Hepatol. 2024 Sep;21(9):609-625. doi: 10.1038/s41575-024-00934-z. Epub 2024 May 28.